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Quantum Delocalization Enables Water Dissociation on Ru(0001) (2412.00484v3)

Published 30 Nov 2024 in cond-mat.mtrl-sci and cond-mat.soft

Abstract: We revisit the long-standing question of whether water molecules dissociate on the Ru(0001) surface through nanosecond-scale path-integral molecular dynamics simulations on a sizable supercell. This is made possible through the development of an efficient and reliable machine-learning potential with near first-principles accuracy, overcoming the limitations of previous ab initio studies. We show that the quantum delocalization associated with nuclear quantum effects enables rapid and frequent proton transfers between water molecules, thereby facilitating the water dissociation on Ru(0001). This work provides the direct theoretical evidence of water dissociation on Ru(0001), resolving the enduring issue in surface sciences and offering crucial atomistic insights into water-metal interfaces.

Summary

  • The paper demonstrates that nuclear quantum effects significantly lower free energy barriers for water dissociation on Ru(0001).
  • It employs advanced computational methods using a moment tensor potential and path-integral molecular dynamics to simulate quantum delocalization.
  • Findings challenge classical thermalization models and offer new insights for designing catalytic processes at metal-water interfaces.

Quantum Delocalization Enables Water Dissociation on Ru(0001)

The interaction of water with metallic surfaces, particularly the Ru(0001) interface, has elicited substantial debate within surface science due to its complexities and implications for fields such as catalysis and corrosion. This paper provides critical insights into one of the most contentious discussions: the dissociation of water on Ru(0001). Through advanced computational approaches, this paper addresses the discrepancies and theoretical uncertainties surrounding this phenomenon, specifically by employing a sophisticated machine-learning potential to capture the underpinnings of water dissociation influenced by nuclear quantum effects (NQEs).

Methodology

Central to this paper is the development and application of a moment tensor potential (MTP), which offers near first-principles accuracy while being computationally efficient. This MTP is employed in conjunction with path-integral molecular dynamics (PIMD) to faithfully simulate the influence of quantum delocalization on water behavior at the Ru(0001) surface. The training dataset was derived from an active learning methodology within VASP, facilitating a comprehensive exploration of potential energy surfaces (PES) across a wide configuration space.

Key Findings

The results demonstrate that NQEs, characterized by quantum delocalization, significantly reduce the free energy barriers for proton transfer, which in turn facilitates rapid water dissociation events. Notably, water dissociation was observed during nanosecond-scale PIMD simulations, which were validated against classical molecular dynamics (MD) simulations that did not account for NQEs and, consequently, did not predict dissociation within similar timescales.

Several specific configurations were scrutinized to identify both intact and dissociative overlayers. The paper refutes the thermalization hypothesis commonly assumed in classical MD simulations and demonstrates that proton quantum delocalization between oxygen atoms, evidenced by broadened O-H and O-O distance distributions, catalyzes hydrogen bonding and dissociation.

Implications

The outcomes of this research have significant implications for surface science and beyond. The findings suggest alternative mechanistic pathways for water dissociation at metal interfaces that should be considered in the design of catalytic processes and other technological applications involving water-metal interfaces. By revealing the impact of quantum delocalization on energy barriers and dissociation pathways, the work challenges prevailing assumptions in surface chemistry and highlights the critical role of quantum effects in hydrogen-related systems.

Moreover, by laying the groundwork for further exploration of quantum mechanical effects in chemical processes at surfaces, these insights illuminate new directions in the development of more accurate predictive models for chemical reactions at metal interfaces.

Future Directions

The implications of this paper suggest a promising avenue for future research: extending these findings to other metal surfaces and reactive environments where quantum effects might similarly influence chemical interactions. Moreover, the advancement in machine learning interatomic potentials as demonstrated provides a template for more extensive and nuanced investigations into complex molecular systems beyond the computational limitations of traditional ab initio methods. As quantum computing technologies continue to mature, integrating these into the exploration of surface chemistry could unveil even more precise understandings of atomic and molecular behavior at these critical interfaces.

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